Optimal activity and battery scheduling algorithm using load and solar generation forecasts
This addresses a specific energy management problem for building operators, but appears incremental as it builds on existing methodologies for forecasting and scheduling.
The paper tackles the problem of reducing electricity bills by scheduling building activities, requiring forecasts of solar generation and consumption, and proposes a technical sequence for forecasting and optimal scheduling, resulting in a solution for a practical competition problem.
Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed. However, in real-world applications, the optimal scheduling problems require reliable energy forecasting, which is scarcely discussed as a joint solution to the scheduling problem. The 5\textsuperscript{th} IEEE Computational Intelligence Society (IEEE-CIS) competition raised a practical problem of decreasing the electricity bill by scheduling building activities, where forecasting the solar energy generation and building consumption is a necessity. To solve this problem, we propose a technical sequence for tackling the solar PV and demand forecast and optimal scheduling problems, where solar generation prediction methods and an optimal university lectures scheduling algorithm are proposed.